What does this “.children” attribute do?

I’m trying to understand a Key-Bigram extractor’s working and I cannot understand what does the following block of code do.

Here is the source code.

import spacy
nlp = spacy.load("en_core_web_sm")

string = '1 2 3 4 5 6 7 8 9'


def textProcessing(doc):
    Words = []
    doc = nlp(doc)

    for possible_words in doc:
        Words.append([possible_words , [child for child in possible_words.children]])
    
    print(Words)

textProcessing(string)
       

Everything else is workin fine and I understood well, however I can not understand what child for child in possible_words.children does.

Answer

token.children uses the dependency parse to get all tokens that directly depend on the token in question. In a visualization (try displacy), this will be all the tokens with arrows pointing away from a token; if the word is a verb this could be the subject and any objects, if the word is a noun it could be any adjectives modifying it, for example.